摘要
针对图表示方法的相关解析任务进行了研究,从形式化定义出发,首先以不同核心技术作为分类标准将图表示学习方法划分为五大类,其包括基于降维解析、矩阵分解、随机游走、深度学习和其他表示学习方法。其次通过归纳与对比分析梳理各类技术发展脉络,进而深层次展现各类图表示方法的优劣。随后结合图表示学习的常用数据集、评估方法和应用领域的归纳分析,展开动态性、可扩展性、可解释性和可解析性的四维剖析。最后总结并展望了图表示学习的未来研究趋势与发展方向。
Research on the related analysis tasks of graph representation methods was carried out,starting from the formal definition,divide graph representation learning methods into five categories with different core technologies as the classification criteria,including dimensionality reduction analysis,matrix decomposition,random walk,neural network and other representation learning methods.Secondly,through induction and comparative analysis,the paper combed the development context of various technologies,and then showed the advantages and disadvantages of various graphic representation methods in a deep level.Then combined with the inductive analysis of common data sets,evaluation methods and application fields of graph representation learning,it carried out the four-dimensional analysis of dynamics,scalability,interpretability and analyticity.Finally,it summarized and looked forward to the future research trends and development directions of graph representation learning.
作者
李青
王一晨
杜承烈
Li Qing;Wang Yichen;Du Chenglie(College of Computer Science,Northwest Polytechnical University,Xi’an 710129,China)
出处
《计算机应用研究》
CSCD
北大核心
2023年第6期1601-1613,共13页
Application Research of Computers
基金
国家自然基金项目青年基金资助项目(62102316)
国家自然科学项目面上项目(62171382)
航空科学基金资助项目(20200051053002)
“十四五”共用信息系统装备预先研究项目。
关键词
图表示
图模型
图表示学习方法
表示学习
深度学习
graph representation
graph model
graph representation learning method
representation learning
deep learning
